Intelligent Data Analysis for Medical Diagnosis

Dear Colleagues,

A medical diagnosis provides an explanation of a patient’s health problem based on symptoms and signs and informs subsequent healthcare decisions. The diagnostic process involves complex and collaborative activities relating to information gathering and clinical reasoning, and therefore, making an accurate diagnosis is a fundamental challenge for global healthcare systems. A study led by Singh revealed that at least 1 in 20 adults in the USA were misdiagnosed every year, equating to 12 million people per year, with half of these misdiagnoses potentially being harmful. On the other hand, advances in information technologies, particularly the ubiquitous nature of mobile technologies adopted in biomedical and health sciences, have generated mountains of data related to health and wellbeing from a wide range of sources, including electronic health records in primary care and secondary care, genome-wide studies, demographics, doctors’ notes, clinical images, laboratory results, genetic tests, wearable sensors, etc. One way to help healthcare professionals improve their diagnostic accuracy is by supporting them to analyse data more efficiently.

The purpose of this Special Issue is to investigate how intelligent data analysis techniques, such as machine learning/deep learning, data mining, big data analytics, etc., hold their promises of more efficiently analysing data to extract useful information and improve clinical decision making and medical diagnosis.

Prof. Dr. Shang-Ming Zhou
Dr. Haider Raza
Dr. Honghan Wu
Guest Editors

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